The research paper I chose was published 2012 and comes from the journal Social Networks, with the impact factor of 3.381. The authors Takhteyev, Gruzd and Wellman wrote Geography of Twitter Networks and is a quantitative study of the different ties between different Twitter-users. Further do they investigate how physical proximity, the traveling distance, national boundaries and language all affects the ties within Twitter.
The authors built a Python script for collecting available public Twitter data during a groundwork period of seven days in their quantitative method. They retrieved 481,288 messages that Twitter made public in their official timeline, the data therefore may not necessarily be random, but it is the data that Twitter chose. During that seven days were around 100 millions Twitter messages published, so they retrieved approximately 0,5 percent of the published data. After retrieving the information did Takhteyev et al. further try to divide and review all of their quantitative data to be able to make a thorough analysis of their findings.
After analyzing the data, the authors concluded that Twitter does not live up to their motto of "Transcending distance, connecting everyone with anyone" since most of the messages connects users within the same regional cluster. For the authors to be able to make such a statement is it demanded that a thorough investigation with a large database of users has been made. Since Takhteyev et al. almost had half a million users in their quantitative study can not the results be ignored, the number of participations brings power to their arguments.
Takhteyev et al. used the data that Twitter had chosen to become public, hence it exist a risk that the released messages do not reflect the ties that a "normal" user on Twitter has. Since they only took around 0,5 percent of all messages can that 0,5 percent be the messages that Twitter consider to be the best ones. If that is the case, the conclusions in the end of the text my be irrelevant. To improve the quality of the quantitative method and the quality of the paper would the authors gain much if they discussed this concern closer and argued that they believed in their chosen method.
I do however believe that their method on collecting data, by accessing public materials and sort them in a Python script, was brilliant. They do not only use a smart approach of collecting and sorting information, they successfully gather enormous amount of data.
Physical Activity, Stress, and Self-Reported Upper Respiratory Tract Infection
In this paper, Fondell, Lagerros, Sundberg, Lekander, Bälter, Rothman and Bälter made a quantitative population-based study, where the authors investigated the relationship between physical activity level, perceived stress and incidence of self-reported upper respiratory tract infection (URTI). They conducted a cohort study of 1509 Swedish men and women aged 20-60 years during a period of 4 month in their paper. They used a Web-based questionnaire to get information from the participants on how they lived, their disease status, their physical activity and their perceived stress. After analyzing the data, they concluded that people with moderate to high physical activity had a lower risk of URTI and that highly stressed people might benefit from physical activity.
There are different ways to gather empirical data when conducting a research paper, in this blog have I mainly mentioned the quantitative methods. The big advantage of using this method type is that you can get input from a huge number of persons. If not much research has been done in the area is it perfect to gather what the concerned masses thinks of the subject. Further can it be easy to compare and find solutions when analyzing the conducted data that supports your hypothesis. That is actually something I think Fondell et al. did well in the text, it felt like they had a clear hypothesis before they began their work, and used the empirical data to show that their speculations were correct.
Another well known method of collecting empirical data is the usage of different qualitative methods. If you have a paper and want a deeper and more thorough examination of the subject will quantitative methods, like interviews or focus groups of experts, give the paper more respect in the academic society. The limitations are that the qualitative methods do not include as many participants as the quantitative methods, hence it hard to get an understanding of what the masses believe to be true.
Interesting study. It seems to prove the saying that "Lika barn leka bäst" (with the english translation courtesy of Wikiquotes "Birds of a feather flock together"), or the fact that you tend to listen to people or ideas that resemble your own. Brilliant idea to collect these huge amount of data using a simple python script.
SvaraRadera